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Proceedings Paper

Locally linear embedding based on local correlation
Author(s): Jing Chen; Yang Liu
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Paper Abstract

The task of nonlinear dimensionality reduction is to find meaningful low-dimensional structures hidden in high dimensional data. In this paper, an unsupervised algorithm for nonlinear dimensionality reduction called locally linear embedding based on local correlation (LC-LLE) is presented. The LC-LLE algorithm is motivated by locally linear embedding (LLE) algorithm and correlation coefficient which usually gives the correlation between two random vectors. It is a major advantage of the LC-LLE to optimize the process of dimensionality reduction by giving more reasonable neighbor searching. Simulation studies demonstrate that the LC-LLE can give better results in dimension reduction than LLE. Experiments on face images data sets have shown the potential of LC-LLE in practical problem.

Paper Details

Date Published: 13 January 2012
PDF: 8 pages
Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83502M (13 January 2012); doi: 10.1117/12.920235
Show Author Affiliations
Jing Chen, Guangdong Univ. of Technology (China)
Yang Liu, Guangdong Univ. of Technology (China)


Published in SPIE Proceedings Vol. 8350:
Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies
Safaa S. Mahmoud; Zhu Zeng; Yuting Li, Editor(s)

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